Granger causality analysis of sleep brain-heart interactions

none4siFaes, Luca; Marinazzo, Daniele; Jurysta, Fabrice; Nollo, GiandomenicoFaes, Luca; Marinazzo, Daniele; Jurysta, Fabrice; Nollo, Giandomenic

Linear and non-linear brain-heart and brain-brain interactions during sleep

In this study, the physiological networks underlying the joint modulation of the parasympathetic component of heart rate variability (HRV) and of the different electroencephalographic (EEG) rhythms during sleep were assessed using two popular measures of directed interaction in multivariate time series, namely Granger causality (GC) and transfer entropy (TE). Time series representative of cardiac and brain activities were obtained in 10 young healthy subjects as the normalized high frequency (HF) component of HRV and EEG power in the \uce\ub4, \uce\ub8, \uce\ub1, \ucf\u83, and \uce\ub2 bands, measured during the whole duration of sleep. The magnitude and statistical significance of GC and TE were evaluated between each pair of series, conditional on the remaining series, using respectively a linear model-based approach exploiting regression models, and a nonlinear model-free approach combining nearest-neighbor entropy estimation with a procedure for dimensionality reduction. The contribution of nonlinear dynamics to the TE was also assessed using surrogate data. GC and TE consistently detected structured networks of physiological interactions, with links directed predominantly from HRV to the EEG waves in the brain-heart network, and from the \ucf\u83 and \uce\ub2 EEG waves to the \uce\ub4, \uce\ub8, and \uce\ub1 waves in the brain-brain network. While these common patterns supported the suitability of a linear model-based analysis, we also found a significant contribution of nonlinear dynamics, particularly involving the information transferred out of the \uce\ub4 node in the two networks. This suggested the importance of nonparametric TE estimation for evidencing the fine structure of the physiological networks underlying the autonomic regulation of cardiac and brain functions during sleep

Information dynamics of brain–heart physiological networks during sleep

his study proposes an integrated approach, framed in the emerging fields of network physiology and information dynamics, for the quantitative analysis of brain-heart interaction networks during sleep. With this approach, the time series of cardiac vagal autonomic activity and brain wave activities measured respectively as the normalized high frequency component of heart rate variability and the EEG power in the δ, θ, α, σ, and β bands, are considered as realizations of the stochastic processes describing the dynamics of the heart system and of different brain sub-systems. Entropy-based measures are exploited to quantify the predictive information carried by each (sub)system, and to dissect this information into a part actively stored in the system and a part transferred to it from the other connected systems. The application of this approach to polysomnographic recordings of ten healthy subjects led us to identify a structured network of sleep brain-brain and brain-heart interactions, with the node described by the β EEG power acting as a hub which conveys the largest amount of information flowing between the heart and brain nodes. This network was found to be sustained mostly by the transitions across different sleep stages, as the information transfer was weaker during specific stages than during the whole night, and vanished progressively when moving from light sleep to deep sleep and to REM sleep

Instability of parasympathetic nerve function evaluated by instantaneous time–frequency analysis in patients with obstructive sleep apnea

The purpose was to determine whether the instability of parasympathetic nerve (PN) function is associated with fragmentation of sleep and the instability can be improved by CPAP treatment in obstructive sleep apnea (OSA). Fifty-three OSA and 50 non-OSA subjects were examined by full-PSG and pulse rate variability (PRV) was recorded simultaneously using a photoplethysmograph and evaluated by instantaneous time-frequency analysis using the complex demodulation method. PN and sympathetic nerve (SN) activity were assessed by the mean high-frequency (HF) amplitude and ratio of low-frequency (LF) and HF amplitude (LF/ HF ratio), respectively. Furthermore, the shift in central frequency (CF) of the main HF peak over time was monitored continuously. The relative times over which the same main HF peak was sustained for at least 20 s and 5 min in total recording time (%HF20s and % HF5min) were considered as markers of PN stability. Twenty-two of 53 patients with OSA also examined under the treatment with continuous positive airway pressure (CPAP). A significant increase in mean LF/ HF ratio and decrease in HF amplitude were observed in severe OSA. Furthermore, both % HF20s and % HF5min were significantly decreased not only in mild-to-moderate OSA but also in severe OSA, and % HF20s was the strongest independent determinant for arousal index. Treatment with CPAP significantly decreased the LH/HF ratio and increased both % HF20s and % HF5min. These findings suggest that the stability of PN function is impaired by arousal due to repeated apnea and hypopnea in OSA, and that CPAP therapy improves SN activity and PN dysfunction.ArticleSLEEP AND BIOLOGICAL RHYTHMS.16(3):323-330(2018)journal articl

Long-term CPAP treatment partially improves the link between cardiac vagal influence and delta sleep.

Continuous positive airway pressure (CPAP) treatment improves the risk of cardiovascular events in patients suffering from severe sleep apnea-hypopnea syndrome (SAHS) but its effect on the link between delta power band that is related to deep sleep and the relative cardiac vagal component of heart rate variability, HF(nu) of HRV, is unknown. Therefore, we tested the hypothesis that CPAP restores the link between cardiac autonomic activity and delta sleep across the night.Journal ArticleResearch Support, Non-U.S. Gov'tSCOPUS: ar.jinfo:eu-repo/semantics/publishe

Temporally Resolved Fluctuation Analysis of Sleep ECG

The correlation behavior in the heart beat rate significantly differs with respect to light sleep, deep sleep, and REM sleep. We investigate whether fluctuations of the heart beat rhythm may serve as a surrogate parameter for rapidly changing sleep phenomena, and if these changes are accessible by progressive beat-by-beat analysis of the sleep electrocardiogram (ECG)